Omnichannel Data Communications System Using Artificial Intelligence (ai) Based Machine Learning and Predictive Analysis

1. A system, comprising: a data store to store and to manage data within a network; a server to facilitate operations using information from the data store; a machine learning (ML)-based artificial intelligence (AI) subsystem to communicate with the server and the data store in the network, the ML-based AI subsystem comprising: a data access interface to: receive data associated with a conversation with a user via a communication channel, the data for training at least one model to determine intent in the conversation with the user; and a processor to: select any unlabeled data from the data associated with the conversation from the user, the selected unlabeled data having probability of impacting the at least one model; provide a hierarchical multi-intent data labeling framework for labeling a representative subset of data from at least one of the data associated with the conversation with the user or the selected unlabeled data; train the at least one model based on the labeled representative subset of data using a plurality of parameter choices; retain at least one candidate model set from the at least one trained models based on global metric performance; generate a production-ready model based on the retained at least one candidate model set, wherein generating the production-ready model comprises: applying a fine-tune intent level evaluation specific to any particular client workflow by providing at least one of the following: providing additional training data from auxiliary models to boost specific intent performance; or providing data oversampling with layer level fine-tuning techniques to correct or augment specific intent performance issues; and creating the production-ready model based on an ensemble of successful fine-tuned candidate models from the retained at least one candidate model set; and deploy the production-ready model for intent determination in the conversation with the user.

2. The system of claim 1 , wherein the communication channel comprises at least one of telephone, email, simple message service (SMS), mobile device application, video conference, website, digital conversational entity, and social network platform.

3. The system of claim 1 , wherein the data access interface further receives data from a data source, the data source comprising at least one of a website, a document, enterprise resource planning (ERP) system, a database, a web feed, a sensor, a geolocation data source, a server, an analytics tool, a mobile device, and a reporting system.

4. The system of claim 1 , wherein the conversation is a multilingual conversation based on the data received from the user or information identifying the user.

5. The system of claim 1 , further wherein cloud-distributed hyper-parameter tuning and ranges for the plurality of parameter choices are predetermined.

6. The system of claim 1 , wherein the plurality of parameter choices is searched and selected randomly via a cloud computing cluster to test development sets to determine which of the at least one model to retain.

7. The system of claim 1 , wherein global metric performance is based on at least one global numerical metric established for each client, wherein the at least one global numerical metric comprising at least one of precision and recall metrics of given intents or importance of identifying particular intents for the given client.

8. The system of claim 1 , wherein deployment of the production-ready model is cloud-agnostic.

9. A method for determining intent in a digital conversation, comprising: receiving, by a processor via a data access interface, data associated with a conversation with a user via a communication channel, the data for training at least one model to determine intent in the conversation with the user; selecting, by the processor, any unlabeled data from the data associated with the conversation from the user, the selected unlabeled data having probability of impacting the at least one model; providing, by the processor, a hierarchical multi-intent data labeling framework for labeling a representative subset of data from at least one of the data associated with the conversation with the user or the selected unlabeled data; training, by the processor, the at least one model based on the labeled representative subset of data using a plurality of parameter choices; retaining, by the processor, at least one candidate model set from the at least one trained models based on global metric performance; generating, by the processor, a production-ready model based on the retained at least one candidate model set, wherein generating the production-ready model comprises: applying a fine-tune intent level evaluation specific to any particular client workflow by providing at least one of the following: providing additional training data from auxiliary models to boost specific intent performance; or providing data oversampling with layer level fine-tuning techniques to correct or augment specific intent performance issues; and creating the production-ready model based on an ensemble of successful fine-tuned candidate models from the retained at least one candidate model set; and deploying, by the processor, the production-ready model for intent determination in the conversation with the user.

10. The method of claim 9 , wherein the communication channel comprises at least one of telephone, email, simple message service (SMS), mobile device application, video conference, website, digital conversational entity, and social network platform.

11. The method of claim 9 , wherein the data access interface further receives data from a data source, the data source comprising at least one of a website, a document, enterprise resource planning (ERP) system, a database, a web feed, a sensor, a geolocation data source, a server, an analytics tool, a mobile device, and a reporting system.

12. The method of claim 9 , wherein the conversation is a multilingual conversation based on the data received from the user or information identifying the user.

13. The method of claim 9 , further wherein cloud-distributed hyper-parameter tuning and ranges for the plurality of parameter choices are predetermined.

14. The method of claim 9 , wherein the plurality of parameter choices is searched and selected randomly via a cloud computing cluster to test development sets to determine which of the at least one model to retain.

15. The method of claim 9 , wherein global metric performance is based on at least one global numerical metric established for each client, wherein the at least one global numerical metric comprising at least one of precision and recall metrics of given intents or importance of identifying particular intents for the given client.

16. The method of claim 9 , wherein deployment of the production-ready model is cloud-agnostic.

17. A non-transitory computer-readable storage medium having an executable stored thereon, which when executed instructs a processor to perform the following: receiving data associated with a conversation with a user via a communication channel, the data for training at least one model to determine intent in the conversation with the user; selecting any unlabeled data from the data associated with the conversation from the user, the selected unlabeled data having probability of impacting the at least one model; providing a hierarchical multi-intent data labeling framework for labeling a representative subset of data from at least one of the data associated with the conversation with the user or the selected unlabeled data; training the at least one model based on the labeled representative subset of data using a plurality of parameter choices; retaining at least one candidate model set from the at least one trained models based on global metric performance; generating a production-ready model based on the retained at least one candidate model set, wherein generating the production-ready model comprises: applying a fine-tune intent level evaluation specific to any particular client workflow by providing at least one of the following: providing additional training data from auxiliary models to boost specific intent performance; or providing data oversampling with layer level fine-tuning techniques to correct or augment specific intent performance issues; and creating the production-ready model based on an ensemble of successful fine-tuned candidate models from the retained at least one candidate model set; and deploying the production-ready model for intent determination in the conversation with the user.